Magnetic resonance imaging (MRI) provides a powerful tool for non-invasive imaging for treatment assessment and for minimally invasive surgery. The contrast sensitivity of the MRI provides a capability of non-invasively revealing structures and functions of internal tissues and organs not known to other imaging techniques such as, for example, CT scan or Ultrasound.
The viability of MRI depends on the image quality it produces. To determine the diagnostic efficacy of a specific MRI, several parameters have been introduced that portray the overall quality of the image data and its capacity to present relevant information to the MRI practitioner. The parameters are: the total time necessary to acquire the data—the scan time, the total time the data is being recorded—the acquisition time, the signal-to-noise ratio (SNR), and the spatial resolution. These parameters are inter-related and, traditionally, an improvement in one of these parameters results in a trade-off in one or more of the others.
The spatial resolution is used to quantify the extent to which different features in an MRI image are distinguishable and accurate delineation of their boundaries is provided. Since the data in MRI are acquired in the Fourier domain, image processing methods that increase the spatial resolution do so by increasing the highest measured frequency. Unfortunately, sampling higher frequencies in k-space decreases the SNR.
Several methods for improving the spatial resolution in MRI have been taught, for example, improving the spatial resolution by estimating frequencies beyond the highest measured frequency. However, estimation of frequencies beyond those measured constitutes a venerable problem in signal and image processing. The means by which a priori information is obtained and included in methods that estimate the higher frequencies for improving the resolution has often been the focus of these techniques. In MRI inclusion of a priori conditions in “constrained reconstruction” methods as disclosed, for example, by Liang Z, Boada F, Constable R, Haacke E M, Lauterbur P, and Smith M: “Constrained Reconstruction Methods in MR Imaging”, Reviews of Magnetic Resonance in Medicine, Vol. 4, pp. 67–185, 1992, have been found to improve the spatial resolution of a single low resolution image with varying levels of success. These methods are able to improve the spatial resolution, but at the risk of introducing new artifacts such as spurious ringing and SNR loss into the image. Spatial resolution enhancement methods utilizing multiple low-resolution images of an object provide an alternate approach to image reconstruction by bringing new a priori conditions to the reconstruction. Super-Resolution (SR) image processing has been first proposed by Tsai R and Huang T: “Multiframe image restoration and registration”, Advances in Computer Vision and Image Processing, JAI Press Inc., Vol. 1, pp. 317–339. The SR image processing attempts to enhance the spatial—or temporal—resolution of an image—or set of images—derived from a sequence of images. The input data are images of a moving scene, for example, taken with a moving imaging apparatus. Super-Resolution MRI (SRMRI) is a recent approach that uses SR techniques in MRI, but has generated some debate in the literature regarding its validity. The first SRMRI approach was disclosed by Roullot E, Herment A, Bloch I, Nikolova M, Mousseaux E: “Regularized reconsctruction of 3D high-resolution magnetic resonance images from acquisitions of anisotropically degraded resolutions”, 15th Int. Conf. on Patt. Rec., September 2000. The underlying physics that relates the desired high resolution image to the acquired low resolution images was not reported. A similar approach was later disclosed by Peled S and Yeshurun Y: “Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging”, Magnetic resonance in Medicine, Vol. 45, pp. 29–35, 2001. The results appeared, on a qualitative level, to be more accurate than those images whose sample spacings were reduced via interpolation. Further investigations of SRMRI have been published by Greenspan H, Peled S, Oz G and Kiryati N: “MRI inter-slice reconstruction using super-resolution”, Proceedings of MICCAI 2001, Fourth International Conference on Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, Springer, October 2001, and Greenspan H, Oz G, Kiryati N and Peled S: “MRI inter-slice reconstruction using super-resolution”, Magnetic Resonance Imaging, Vol. 20, pp. 437–446, 2002. However, the underlying MR physics relating the low-resolution images and the desired high-resolution image has not been reported, prompting skepticism regarding the validity of SR techniques in MRI as discussed in Scheffler K: “Superresolution in MRI?”, Magnetic Resonance in Medicine, Vol. 48, p. 408, 2002, and in Peled S and Yeshurun Y: “Superresolution in MRI-perhaps sometimes”, Magnetic Resonance in Medicine, Vol. 48, p. 409, 2002.
It would be advantageous to be able to overcome the shortcomings of the prior art by providing a MR image reconstruction process that is capable of synthesizing a high-resolution MR image that has a higher spatial resolution as well as a higher SNR. Furthermore, it would be advantageous to incorporate the underlying physics relating the low-resolution images and the desired high-resolution image in order to provide a more accurate estimate of the object being imaged.